AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices

TL;DR

This paper reviews recent AI techniques applied to dental CBCT imaging, highlighting their potential to enhance diagnosis and treatment planning, while discussing current challenges and limitations in the field.

Contribution

It provides a comprehensive overview of AI trends and practices in dental CBCT imaging, emphasizing recent advancements and future potential.

Findings

AI improves lesion detection accuracy

Deep learning enhances segmentation of dental structures

Super-resolution techniques increase image clarity

Abstract

Cone-beam computed tomography (CBCT) is a popular imaging modality in dentistry for diagnosing and planning treatment for a variety of oral diseases with the ability to produce detailed, three-dimensional images of the teeth, jawbones, and surrounding structures. CBCT imaging has emerged as an essential diagnostic tool in dentistry. CBCT imaging has seen significant improvements in terms of its diagnostic value, as well as its accuracy and efficiency, with the most recent development of artificial intelligence (AI) techniques. This paper reviews recent AI trends and practices in dental CBCT imaging. AI has been used for lesion detection, malocclusion classification, measurement of buccal bone thickness, and classification and segmentation of teeth, alveolar bones, mandibles, landmarks, contours, and pharyngeal airways using CBCT images. Mainly machine learning algorithms, deep learning algorithms, and super-resolution techniques are used for these tasks. This review focuses on the potential of AI techniques to transform CBCT imaging in dentistry, which would improve both diagnosis and treatment planning. Finally, we discuss the challenges and limitations of artificial intelligence in dentistry and CBCT imaging.